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33 articles
This explainer explores how advanced AI systems can generate music that mimics human artists, examining the neural network architectures, pattern recognition techniques, and broader implications for intellectual property and creative industries.
Learn how to set up an AI development environment, load pre-trained models, and work with neural networks using Python and popular libraries like PyTorch and TensorFlow.
Learn how to repurpose pre-trained video generators for classic computer vision tasks like depth estimation and semantic segmentation, demonstrating the potential of video generators as universal world models.
Learn to build and train world model architectures using PyTorch, understanding the technology behind AMI Labs' approach to AI development.
Learn to build a vision-language-action model for warehouse robotics that can reduce human intervention by half, similar to Nomagic's breakthrough implementation.
Learn how LingBot-Vision, an open-source AI model from Ant Group, uses boundary-focused learning to understand spatial relationships in images, with applications in robotics, self-driving cars, and more.
This article explains the advanced AI concepts behind modern smartwatches and fitness trackers, including machine learning architectures, personalized health algorithms, and edge computing techniques used in wearable health monitoring.
Prime Intellect releases prime-rl 0.6.0, an open framework for training trillion-parameter Mixture-of-Experts models using asynchronous reinforcement learning.
Learn about the In the Weights score, a novel AI evaluation metric that analyzes neural network parameters to predict model performance and optimize training.
Learn how xFormers helps make AI models faster and more memory-efficient by optimizing how they process text data.
Learn to use MisoTTS, an 8B emotive text-to-speech model with open weights, to generate emotionally expressive speech by conditioning on both text and audio context.
Learn to implement and experiment with fundamental AI concepts including neural networks, transformers, and attention mechanisms through hands-on coding exercises.
